R Notebook

# Load and process data
lv.data <- read.csv("/Users/sritejpadmanabhan/Desktop/Research/ShareWithSritej/Exp1/Exp1_lvdata_7June2024.csv", header = TRUE, sep = ',')
print("Data loaded successfully")

[1] "Data loaded successfully"

print(head(lv.data))

if(nrow(lv.data) == 0) stop("Data not loaded correctly or file is empty")

# Summarize the data
lv.sum.data <- lv.data %>%
  filter(CN %in% c(11:15, 16:20, 61:65, 66:70)) %>%
  mutate(Phase = case_when(
    CN >= 11 & CN <= 15 ~ 'Baseline',
    CN >= 16 & CN <= 20 ~ 'Early Adapt',
    CN >= 61 & CN <= 65 ~ 'Late Adapt',
    CN >= 66 & CN <= 70 ~ 'Aftereffect'
  )) %>%
  group_by(SN, Phase, Group, Subject.ID, Gender, YOE_pro, age, Road_Signs, Handedness, VF_central, VF_peripheral, Acuity_RE, Acuity_LE, Cause_of_Impairment, Diagnosis, Duration_of_LV, Low_Vision_Onset, Diagnosis_Category, Technology_Proficiency, Device, Difficulty_with_steps, Difficulty_with_Stairs, Difficulty_with_pouring, Difficulty_with_Obstacles) %>%
  summarise(
    Hand_mean = mean(HandFlip, na.rm = TRUE),
    RT = mean(RT, na.rm = TRUE),
    RTb = mean(RTb, na.rm = TRUE),
    MT = mean(MT, na.rm = TRUE),
    ST = mean(ST, na.rm = TRUE)
  ) %>%
  ungroup() %>%
  mutate(
    Acuity_RE = log10(Acuity_RE / 20),
    Acuity_LE = log10(Acuity_LE / 20),
    min_Acuity = pmin(Acuity_RE, Acuity_LE),
    mean_Acuity = (Acuity_RE + Acuity_LE) / 2
  ) %>%
  mutate(
    VF_P = ifelse(as.character(VF_peripheral) == 'No Impairment', 'Per. Intact', 'Per. Impaired'),
    VF_C = ifelse(as.character(VF_central) == 'No Impairment', 'Cen. Intact', 'Cen. Impaired'),
    VF_P = ifelse(Group == 'Control', 'Per. Intact', VF_P),
    VF_C = ifelse(Group == 'Control', 'Cen. Intact', VF_C),
    Acuity = ifelse(min_Acuity > 0.8890756, 'Low Acuity', 'High Acuity'),
    Acuity = ifelse(Group == 'Control', 'Control', Acuity)
  ) %>%
  mutate(Road_Acuity = ifelse(Road_Signs > 3, 'Higher Acuity', 'Lower Acuity')) %>%
  mutate(Split_age = case_when(
    Group == 'Control' & age > 41 ~ 'Old',
    Group == 'Control' & age <= 41 ~ 'Young',
    Group == 'LV' & age > 59 ~ 'Old',
    Group == 'LV' & age <= 59 ~ 'Young'
  )) %>%
  mutate(ChangeRT = RT - lag(RT))  # only works for early RT

`summarise()` has grouped output by 'SN', 'Phase', 'Group', 'Subject.ID', 'Gender', 'YOE_pro', 'age',
'Road_Signs', 'Handedness', 'VF_central', 'VF_peripheral', 'Acuity_RE', 'Acuity_LE', 'Cause_of_Impairment',
'Diagnosis', 'Duration_of_LV', 'Low_Vision_Onset', 'Diagnosis_Category', 'Technology_Proficiency', 'Device',
'Difficulty_with_steps', 'Difficulty_with_Stairs', 'Difficulty_with_pouring'. You can override using the
`.groups` argument.

print("Data summarized successfully")

[1] "Data summarized successfully"

print(head(lv.sum.data))

NA
NA

# Factor levels for various columns
lv.sum.data$VF_central <- factor(lv.sum.data$VF_central, levels = c('No Impairment', 'Mild', 'Moderate', 'Severe', ' '))
lv.sum.data$VF_peripheral <- factor(lv.sum.data$VF_peripheral, levels = c('No Impairment', 'Mild', 'Moderate', 'Severe', ' '))
lv.sum.data$Phase <- factor(lv.sum.data$Phase, levels = c('Baseline', 'Early Adapt', 'Late Adapt', 'Aftereffect'))
lv.sum.data$VF_C <- factor(lv.sum.data$VF_C, levels = c('Cen. Intact', 'Cen. Impaired'))
lv.sum.data$VF_P <- factor(lv.sum.data$VF_P, levels = c('Per. Intact', 'Per. Impaired'))
lv.sum.data$Road_Acuity <- factor(lv.sum.data$Road_Acuity, levels = c('Higher Acuity', 'Lower Acuity'))

# Summarize individual data
lv.ind.data <- lv.data %>%
  group_by(SN, CN, Block, Group) %>%
  summarise(
    Hand_ind_mean = mean(HandFlip, na.rm = TRUE),
    RT_ind_mean = mean(RT, na.rm = TRUE),
    MT_ind_mean = mean(MT, na.rm = TRUE),
    ST_ind_mean = mean(ST, na.rm = TRUE)
)

`summarise()` has grouped output by 'SN', 'CN', 'Block'. You can override using the `.groups` argument.

print("Individual data summarized successfully")

[1] "Individual data summarized successfully"

print(head(lv.ind.data))

NA

# Summarize group-level data
lv.grp.data <- lv.ind.data %>%
  group_by(CN, Block, Group) %>%
  summarise(
    Hand_grp_mean = mean(Hand_ind_mean, na.rm = TRUE),
    Hand_grp_sem = std.error(Hand_ind_mean, na.rm = TRUE),
    RT_grp_mean = mean(RT_ind_mean, na.rm = TRUE),
    RT_grp_sem = std.error(RT_ind_mean, na.rm = TRUE),
    MT_grp_mean = mean(MT_ind_mean, na.rm = TRUE),
    MT_grp_sem = std.error(MT_ind_mean, na.rm = TRUE),
    ST_grp_mean = mean(ST_ind_mean, na.rm = TRUE),
    ST_grp_sem = std.error(ST_ind_mean, na.rm = TRUE)
)

`summarise()` has grouped output by 'CN', 'Block'. You can override using the `.groups` argument.

print("Group data summarized successfully")

[1] "Group data summarized successfully"

print(head(lv.grp.data))

NA

# Plot all group data
lv.all.plot <- lv.grp.data %>%
  ggplot(aes(x = CN, y = Hand_grp_mean, group = interaction(factor(Block), Group), color = Group, fill = Group)) +
  annotate('segment', x = c(0, 0, 15.5, 65.5), xend = c(75, 75, 15.5, 65.5), y = c(0, 45, -5, -5), yend = c(0, 45, 50, 50), color = 'lightgrey') +
  geom_line() +
  geom_ribbon(aes(ymin = Hand_grp_mean - Hand_grp_sem, ymax = Hand_grp_mean + Hand_grp_sem), alpha = 0.2, color = NA) +
  th + theme(legend.position = 'none') +
  scale_y_continuous(limits = c(-5, 50), breaks = seq(0, 45, 15)) +
  scale_x_continuous(limits = c(0, 75), breaks = c(0, 15, 66, 75)) +
  scale_color_manual(values = c('darkgreen', 'darkmagenta')) +
  scale_fill_manual(values = c('darkgreen', 'darkmagenta')) +
  labs(x = 'Movement Cycle (3 Reaches)', y = 'Hand Angle (°)')

print("Plotting all group data")

[1] "Plotting all group data"

print(lv.all.plot)

# Plot summarized hand data
lv.hand.sum.plot <- lv.sum.data %>%
  filter(Phase != 'Baseline') %>%
  gather(Cond, Value, Hand_mean:ST) %>%
  filter(Cond == 'Hand_mean') %>%
  ggplot(aes(x = Phase, group = interaction(Phase, Group), y = Value, color = Group)) +
  geom_hline(yintercept = c(0), color = "grey") +
  geom_boxplot(outlier.shape = NA) +
  th +
  theme(legend.position = "none") +
  geom_point(position = position_jitterdodge(0.75, jitter.width = 0.2), alpha = 0.3, size = 0.8) +
  stat_summary(fun = "mean", geom = "errorbar", position = position_dodge(0.75), aes(ymax = ..y.., ymin = ..y..), linetype = "dashed", width = 0.70) +
  scale_y_continuous(breaks = seq(-60, 60, 30)) +
  coord_capped_cart(ylim = c(-60, 80)) +
  scale_color_manual(values = my.colors2) +
  labs(x = '', y = 'Hand Angle (°)')

print("Plotting summarized hand data")

[1] "Plotting summarized hand data"

print(lv.hand.sum.plot)

# Enhanced Analysis: Calculate correlations and perform t-tests
cor_test_results <- cor.test(lv.sum.data$Hand_mean, lv.sum.data$RT, method = "pearson")
t_test_results <- t.test(lv.sum.data$Hand_mean ~ lv.sum.data$Group)

# Print statistical test results
print("Correlation Test Results")

[1] "Correlation Test Results"

print(cor_test_results)

    Pearson's product-moment correlation

data:  lv.sum.data$Hand_mean and lv.sum.data$RT
t = 0.61504, df = 318, p-value = 0.539
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.07545585  0.14356679
sample estimates:
       cor 
0.03446934 

print("T-Test Results")

[1] "T-Test Results"

print(t_test_results)

    Welch Two Sample t-test

data:  lv.sum.data$Hand_mean by lv.sum.data$Group
t = -0.41174, df = 317.94, p-value = 0.6808
alternative hypothesis: true difference in means between group Control and group LV is not equal to 0
95 percent confidence interval:
 -3.730268  2.439163
sample estimates:
mean in group Control      mean in group LV 
             14.74431              15.38986 

# Additional plot: Relationship between Hand Mean and Reaction Time
lv.hand_vs_rt.plot <- lv.sum.data %>%
  ggplot(aes(x = Hand_mean, y = RT, color = Group)) +
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE) +
  th +
  labs(x = 'Hand Mean (°)', y = 'Reaction Time (ms)', title = 'Relationship between Hand Mean and Reaction Time') +
  scale_color_manual(values = my.colors2)

print("Plotting relationship between Hand Mean and Reaction Time")

[1] "Plotting relationship between Hand Mean and Reaction Time"

print(lv.hand_vs_rt.plot)

`geom_smooth()` using formula = 'y ~ x'

# Additional plot: Distribution of Hand Mean by Group
lv.hand_mean_dist.plot <- lv.sum.data %>%
  ggplot(aes(x = Hand_mean, fill = Group)) +
  geom_histogram(binwidth = 1, alpha = 0.7, position = "identity") +
  th +
  labs(x = 'Hand Mean (°)', y = 'Count', title = 'Distribution of Hand Mean by Group') +
  scale_fill_manual(values = my.colors2)

print("Plotting distribution of Hand Mean by Group")

[1] "Plotting distribution of Hand Mean by Group"

print(lv.hand_mean_dist.plot)